Recruitment, augmentation and apoptosis of rat osteoclasts in 1,25-(OH)2D3 response to short-term treatment with 1,25-dihydroxyvitamin D3in vivo

Background Although much is known about the regulation of osteoclast (OC) formation and activity, little is known about OC senescence. In particular, the fate of of OC seen after 1,25-(OH)2D3 administration in vivo is unclear. There is evidence that the normal fate of OC is to undergo apoptosis (programmed cell death). We have investigated the effect of short-term application of high dose 1,25-dihydroxyvitamin D3 (1,25-(OH)2D3) on OC apoptosis in an experimental rat model. Methods OC recruitment, augmentation and apoptosis was visualised and quantitated by staining histochemically for tartrate resistant acid phosphatase (TRAP), double staining for TRAP/ED1 or TRAP/DAPI, in situ DNA fragmentation end labelling and histomorphometric analysis. Results Short-term treatment with high-dose 1,25-(OH)2D3 increased the recruitment of OC precursors in the bone marrow resulting in a short-lived increase in OC numbers. This was rapidly followed by an increase in the number of apoptotic OC and their subsequent removal. The response of OC to 1,25-(OH)2D3 treatment was dose and site dependent; higher doses producing stronger, more rapid responses and the response in the tibiae being consistently stronger and more rapid than in the vertebrae. Conclusions This study demonstrates that (1) after recruitment, OC are removed from the resorption site by apoptosis (2) the combined use of TRAP and ED1 can be used to identify OC and their precursors in vivo (3) double staining for TRAP and DAPI or in situ DNA fragmentation end labelling can be used to identify apoptotic OC in vivo

The Long Memory of Equity Volatility: International Evidence

This paper examines long memory volatility in international stock markets. We show that long memory volatility is widespread in eighty-two countries and that the degree of memory can be related to macroeconomic variables such as inflation, unemployment rates, interest rates or stability of a country measured by jumps. The relationships hold both in the time-series and the cross-sectional dimension. We also find that developed countries possess longer memory in volatility than emerging and frontier countries

Scaling of the distribution of fluctuations of financial market indices

We study the distribution of fluctuations over a time scale $\Delta t$ (i.e., the returns) of the S&P 500 index by analyzing three distinct databases. Database (i) contains approximately 1 million records sampled at 1 min intervals for the 13-year period 1984-1996, database (ii) contains 8686 daily records for the 35-year period 1962-1996, and database (iii) contains 852 monthly records for the 71-year period 1926-1996. We compute the probability distributions of returns over a time scale $\Delta t$, where $\Delta t$ varies approximately over a factor of 10^4 - from 1 min up to more than 1 month. We find that the distributions for $\Delta t \leq$ 4 days (1560 mins) are consistent with a power-law asymptotic behavior, characterized by an exponent $\alpha \approx 3$, well outside the stable L\'evy regime $0 < \alpha < 2$. To test the robustness of the S&P result, we perform a parallel analysis on two other financial market indices. Database (iv) contains 3560 daily records of the NIKKEI index for the 14-year period 1984-97, and database (v) contains 4649 daily records of the Hang-Seng index for the 18-year period 1980-97. We find estimates of $\alpha$ consistent with those describing the distribution of S&P 500 daily-returns. One possible reason for the scaling of these distributions is the long persistence of the autocorrelation function of the volatility. For time scales longer than $(\Delta t)_{\times} \approx 4$ days, our results are consistent with slow convergence to Gaussian behavior.Comment: 12 pages in multicol LaTeX format with 27 postscript figures (Submitted to PRE May 20, 1999). See http://polymer.bu.edu/~amaral/Professional.html for more of our work on this are

Discrete-time volatility forecasting with persistent leverage effect and the link with continuous-time volatility modeling

We first propose a reduced-form model in discrete time for S&P 500 volatility showing that the forecasting performance can be significantly improved by introducing a persistent leverage effect with a long-range dependence similar to that of volatility itself. We also find a strongly significant positive impact of lagged jumps on volatility, which however is absorbed more quickly. We then estimate continuous-time stochastic volatility models that are able to reproduce the statistical features captured by the discrete-time model. We show that a single-factor model driven by a fractional Brownian motion is unable to reproduce the volatility dynamics observed in the data, while a multifactor Markovian model fully replicates the persistence of both volatility and leverage effect. The impact of jumps can be associated with a common jump component in price and volatility